The Challenges of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing in low-middle income countries and possible cost-effective measures in resource-limited settings
Bibliographic record
Abstract
Diagnostic testing for the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection remains a challenge around the world, especially in low-middle-income countries (LMICs) with poor socio-economic backgrounds. From the beginning of the pandemic in December 2019 to August 2021, a total of approximately 3.4 billion tests were performed globally. The majority of these tests were restricted to high income countries. Reagents for diagnostic testing became a premium, LMICs either cannot afford or find manufacturers unwilling to supply them with expensive analytical reagents and equipment. From March to December 2020 obtaining testing kits for SARS-CoV-2 testing was a challenge. As the number of SARS-CoV-2 infection cases increases globally, large-scale testing still remains a challenge in LMICs. The aim of this review paper is to compare the total number and frequencies of SARS-CoV-2 testing in LMICs and high-income countries (HICs) using publicly available data from Worldometer COVID-19, as well as discussing possible interventions and cost-effective measures to increase testing capability in LMICs. In summary, HICs conducted more SARS-CoV-2 testing (USA: 192%, Australia: 146%, Switzerland: 124% and Canada: 113%) compared to middle-income countries (MICs) (Vietnam: 43%, South Africa: 29%, Brazil: 27% and Venezuela: 12%) and low-income countries (LICs) (Bangladesh: 6%, Uganda: 4% and Nigeria: 1%). Some of the cost-effective solutions to counteract the aforementioned problems includes using saliva instead of oropharyngeal or nasopharyngeal swabs, sample pooling, and testing high-priority groups to increase the number of mass testing in LMICs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".